Currently, however, XAI lacks a clear formalization as it resembles more of a loose collection of methods, for which again it is not clearly defined when a method is explainable. The largest subgroup of XAI is interpretable machine learning (IML), concerned with the explanation of machine and deep learning models. Other subgroups include, e.g., interpretable reinforcement learning [1]} or interpretable evolutionary algorithms [2]}. In this work, we argue that one possible formalization of IML can be by means of statistics, indicating that we might have been doing classical statistics all along. First, we define IML as a process leading from correlation to causation. Furthermore, we elaborate on conceptual similarities between IML and classical statistics and infer a formalization of IML along the lines of a statistical process. We argue, that through our formalization, IML can be defined as a statistical process leveraging machine learning models and query methods, viewing IML methods as sophisticated statistical tools to support or contradict hypotheses. At last, we present three key questions elaborated from this view, which we argue are crucial for the success and adoption of IML in real-world and safety-critical settings.
